IXA-PIPES (Apache Licensed NLP Tool )

To meature Accuracy:

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IXA pipes is a modular set of Natural Language Processing tools (or pipes) which provide easy access to NLP technology for several languages. It offers robust and efficient linguistic annotation to both researchers and non-NLP experts with the aim of lowering the barriers of using NLP technology either for research purposes or for small industrial developers and SMEs. The ixa pipes can be used or exploit its modularity to pick and change different components. The tools are developed by the IXA NLP Group of the University of the Basque Country.

ixa pipes

If you use the ixa pipes tools or the models, please cite this paper:

Rodrigo Agerri, Josu Bermudez and German Rigau (2014): "IXA pipeline: Efficient and Ready to Use Multilingual NLP tools", in: Proceedings of the 9th Language Resources and Evaluation Conference (LREC2014), 26-31 May, 2014, Reykjavik, Iceland. PDF paper

ixa-pipe-tok: Tokenizer and Segmenter for several languages.

ixa-pipe-pos: Statistical POS tagging and Lemmatizer for Basque, Dutch, English, French, Galician, German, Italian and Spanish.

ixa-pipe-nerc: Named Entity Recognition tagger for Basque, Spanish, English, German, Dutch and Italian; Opinion Target Extraction (OTE) for English.

ixa-pipe-chunk: Probabilistic chunker for Basque and English.

ixa-pipe-parse: Probabilistic constituent parser for Spanish and English.

Every ixa pipe can be up an running after two simple steps. The tools require Java 1.7+ to run and are designed to come with all batteries included, which means that it is not required to do any system configuration or install any third-party dependencies. The modules will run on any platform as long as a JVM 1.7+ is available.

IXA pipes are just a set of processes chained by their standard streams, in a way that the output of each process feeds directly as input to the next one. The Unix pipes metaphor has been applied for NLP tools by adopting a very simple and well known data centric architecture, in which every module/pipe is interchangeable by any other tool as long as it reads and writes the required data format via the standard streams.

The data format in which both the input and output of the modules needs to be formatted to represent and pipe linguistic annotations is NAF. Our Java modules all use the kaflib library for easy NAF integration.

1.1.1 Release distribution

Download the ready to use binaries and source code with every model included here:

ixa-pipes-1.1.1.tar.gz [500M]

Development

The ixa pipes are developed in github. Clone those repos and follow instructions for the latest versions of the tools. We also gladly accept pull requests!!

• ixa-pipe-tok repo
• ixa-pipe-pos repo
• ixa-pipe-nerc repo
• ixa-pipe-chunk repo
• ixa-pipe-parse repo

Licensing

ixa-pipes are distributed under the Apache License 2.0 (APL 2.0).

Third party tools

The ixa pipes are extended with third party tools for other linguistic annotations, such as word sense disambiguation, semantic role labelling, named entity disambiguation and wikification against the DBpedia, and coreference resolution. Go to the third party tools page for information about how to download and use each tool.

Notice!!

If you are still using release 1.0.0 please update (there is a claim that 1.0.0 might contain GPL code). Please update also ixa-pipe-tok to version 1.8.+ which is non-controversially APL 2.0.

Setup Apache OPENNLP Java Project in Eclipse

In this openNLP tutorial, we shall see how to setup OPENNLP java project to use OPENNLP API with Eclipse (the process should be same, to other IDEs as well).

Following are the steps to be followed :

1. Create a Java Project in the Eclipse. (Open Eclipse -> File(in Menu) -> New -> Project -> Java -> Java Project)
2. Provide a project name (Ex : OpenNLPJavaTutorial) and click on “Finish”.
3. Download jar files of openNLP from http://redrockdigimark.com/apachemirror/opennlp/
   At the time of writing this tutorial, opennlp-1.7.1 is the latest, and the list looks like in the below picture
   
   

   opennlp version links
   
   Click on opennlp-1.7.1/ . We need bin package, because that could have the library (.jar) files.
   
   
   

   openNLP bin package
   Click on apache-opennlp-1.7.1-bin.zip to download.
4. Once the zip file is downloaded, extract the contents, copy the lib folder and paste in the project as shown in the below picture.
   
   

   opennlp-java-project-lib folder
   Lib folder should contain the list of below jar files:
   aopalliance-repackaged-2.5.0-b30.jar
   grizzly-framework-2.3.28.jar
   grizzly-http-2.3.28.jar
   grizzly-http-server-2.3.28.jar
   hk2-api-2.5.0-b30.jar
   hk2-locator-2.5.0-b30.jar
   hk2-utils-2.5.0-b30.jar
   hppc-0.7.1.jar
   jackson-annotations-2.8.4.jar
   jackson-core-2.8.4.jar
   jackson-databind-2.8.4.jar
   jackson-jaxrs-base-2.8.4.jar
   jackson-jaxrs-json-provider-2.8.4.jar
   jackson-module-jaxb-annotations-2.8.4.jar
   javassist-3.20.0-GA.jar
   javax.annotation-api-1.2.jar
   javax.inject-2.5.0-b30.jar
   javax.ws.rs-api-2.0.1.jar
   jcommander-1.48.jar
   jersey-client-2.25.jar
   jersey-common-2.25.jar
   jersey-container-grizzly2-http-2.25.jar
   jersey-entity-filtering-2.25.jar
   jersey-guava-2.25.jar
   jersey-media-jaxb-2.25.jar
   jersey-media-json-jackson-2.25.jar
   jersey-server-2.25.jar
   morfologik-fsa-2.1.0.jar
   morfologik-fsa-builders-2.1.0.jar
   morfologik-stemming-2.1.0.jar
   morfologik-tools-2.1.0.jar
   opennlp-brat-annotator-1.7.1.jar
   opennlp-morfologik-addon-1.7.1.jar
   opennlp-tools-1.7.1.jar
   opennlp-uima-1.7.1.jar
   osgi-resource-locator-1.0.1.jar
   validation-api-1.1.0.Final.jar
5. Add these jars to the build path (Project -> Properties -> Java Build Path -> Libraries -> Add Jars -> Select all the jars in lib folder -> Click “Apply” -> Click “OK”)
6. Apache has already trained some models for different problems in Natural Language Processing, with training data, and these models are available at http://opennlp.sourceforge.net/models-1.5/ . In the subsequent tutorials, we would refer to model files, which are available at this location. Do bookmark the link for a quick access.
7. We are ready with the openNLP Java Project Setup. Lets try Sentence detection using SentenceDetectExample.java.
8. Download “en-sent.bin” model file and place in the project. The final project structure should match with the structure shown in the below picture
   
   

   opennlp java project structure

Example : We shall try out the example, SentenceDetectExample.java to check if the setup is good

SentenceDetectExample.java

Java

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import java.io.FileInputStream; import java.io.IOException; import java.io.InputStream; import com.fasterxml.jackson.databind.exc.InvalidFormatException; import opennlp.tools.sentdetect.SentenceDetectorME; import opennlp.tools.sentdetect.SentenceModel; /** * @author tutorialkart */ public class SentenceDetectExample {     public static void main(String[] args) {         try {             new SentenceDetectExample().sentenceDetect();         } catch (IOException e) {             e.printStackTrace();         }     }     public void sentenceDetect() throws InvalidFormatException, IOException {         String paragraph = "Apache openNLP supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning.";         // refer to model file "en-sent,bin", available at link http://opennlp.sourceforge.net/models-1.5/         InputStream is = new FileInputStream("en-sent.bin");         SentenceModel model = new SentenceModel(is);         // load the model         SentenceDetectorME sdetector = new SentenceDetectorME(model);         // detect sentences in the paragraph         String sentences[] = sdetector.sentDetect(paragraph);         // print the sentences detected, to console         for(int i=0;i<sentences.length;i++){             System.out.println(sentences[i]);         }         is.close();     } }

When SentenceDetectExample.java is run, the console output is:

Program Output

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Apache openNLP supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning.

We are successfully done with the setup of openNLP Java Project in Eclipse.

Conclusion :

In this openNLP tutorial, we have seen the setup of openNLP Java Project in Eclipse. In our next openNLP tutorials, we shall see :

• Named Entity Extraction